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        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411
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        Tool Citations

        Please remember to cite the tools that you use in your analysis.

        To help with this, you can download publication details of the tools mentioned in this report:

        About MultiQC

        This report was generated using MultiQC, version 1.27

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/MultiQC/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        This report has been generated by the nf-core/viralmetagenome analysis pipeline. For information about how to interpret these results, please see the documentation.
        Report generated on 2025-06-22, 14:08 CEST based on data in:
        • /scratch/leuven/344/vsc34477/work/3b/021d9f59974b6b56b3fdbc1d87ee65/multiqc_files
        • /vsc-hard-mounts/leuven-data/344/vsc34477/LVE-BIO2-PIPELINE/viralgenie/assets/multiqc_config.yml

        General Statistics

        Showing 7/7 rows and 31/37 columns.
        Sample Name% Dups Input Reads% GC Input ReadsAverage Length Input ReadsMedian len% Failed Input ReadsNr. Input Reads% Duplication% > Q30Mb Q30 basesReads After FilteringGC content% PF% Adapter% Dups reads post Trimming% GC reads post TrimmingAverage Length reads post TrimmingMedian len% Failed reads post TrimmingNr. reads post Trimmingsynthetic construct% reads of top 5 host/contaminants% non-host reads% Dups Processed Reads% GC Processed ReadsAverage Length Processed ReadsMedian len% Failed Processed ReadsNr. Processed ReadsN50 (Spades)Total length (Spades)N50 (Megahit)Total length (Megahit)# Clusters# Removed clustersAverage cluster sizeNumber of singletons# Final denovo clusters
        DRX474368
        83.3%
        48.0%
        151bp
        151bp
        27%
        32128673.0M
        52.1%
        98.8%
        6588.9Mb
        48201648.0
        47.6%
        75.0%
        42.0%
        90.1%
        47.0%
        138bp
        147bp
        18%
        24100824.0M
        0.0%
        21.0%
        60.9%
        88.2%
        44.0%
        138bp
        151bp
        30%
        14682633.0M
        1.1Kbp
        24.7Mbp
        1.3Kbp
        21.6Mbp
        5
        1011
        12.4
        0
        3
        SRX16007174
        71.6%
        46.0%
        101bp
        101bp
        20%
        17926016.0M
        27.4%
        99.2%
        3058.3Mb
        30624614.0
        46.0%
        85.4%
        1.3%
        69.8%
        45.0%
        100bp
        100bp
        10%
        15312307.0M
        0.0%
        4.8%
        87.8%
        67.1%
        45.0%
        100bp
        100bp
        20%
        13450178.0M
        1.1Kbp
        41.3Mbp
        1.4Kbp
        35.0Mbp
        5
        834
        1.6
        0
        4
        SRX17981498
        94.1%
        52.0%
        134bp
        142bp
        30%
        2437832.0M
        76.9%
        98.6%
        279.4Mb
        2115198.0
        52.3%
        86.8%
        95.3%
        52.0%
        134bp
        142bp
        30%
        2115198.0M
        0.0%
        37.2%
        9.0%
        77.2%
        44.0%
        135bp
        147bp
        40%
        190359.0M
        0.3Kbp
        0.1Mbp
        1.1Kbp
        0.0Mbp
        3
        6
        7.0
        0
        3
        SRX25387944
        65.4%
        43.0%
        147bp
        150bp
        9%
        25130311.0M
        27.4%
        97.5%
        6283.1Mb
        43827288.0
        44.0%
        87.2%
        2.4%
        64.4%
        43.0%
        147bp
        150bp
        9%
        21913644.0M
        0.0%
        0.9%
        95.1%
        64.0%
        43.0%
        147bp
        150bp
        10%
        20848330.0M
        0.7Kbp
        70.8Mbp
        1.0Kbp
        63.1Mbp
        5
        1298
        5.6
        0
        5
        SRX26432135
        54.7%
        54.0%
        150bp
        150bp
        18%
        23274889.0M
        17.0%
        98.9%
        5761.2Mb
        39401022.0
        54.5%
        84.6%
        6.5%
        57.9%
        54.0%
        148bp
        150bp
        18%
        19700511.0M
        0.0%
        2.0%
        91.8%
        54.6%
        54.0%
        148bp
        150bp
        20%
        18092591.0M
        3.3Kbp
        27.6Mbp
        3.7Kbp
        26.6Mbp
        5
        145
        0.8
        1
        5
        SRX27477944
        47.2%
        42.0%
        150bp
        150bp
        10%
        21583067.0M
        7.8%
        96.4%
        3701.1Mb
        25777148.0
        41.9%
        59.7%
        7.5%
        42.5%
        41.0%
        149bp
        150bp
        10%
        12888574.0M
        0.0%
        0.0%
        96.6%
        42.3%
        41.0%
        149bp
        150bp
        10%
        12448828.0M
        0.7Kbp
        84.1Mbp
        1.1Kbp
        77.4Mbp
        5
        1534
        5.0
        0
        4
        SRX28813238
        84.5%
        40.0%
        147bp
        151bp
        45%
        9893934.0M
        43.5%
        95.9%
        1952.7Mb
        13974266.0
        41.2%
        70.6%
        2.3%
        84.2%
        41.0%
        146bp
        151bp
        36%
        6987133.0M
        0.0%
        11.8%
        82.3%
        81.6%
        39.0%
        146bp
        151bp
        30%
        5748794.0M
        0.5Kbp
        35.1Mbp
        0.7Kbp
        24.2Mbp
        4
        1068
        2.8
        1
        4

        SAMPLE: FastQC (Raw)

        Quality control tool for high throughput sequencing data.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        Created with MultiQC

        Sequence Quality Histograms

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        Created with MultiQC

        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        Created with MultiQC

        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        Created with MultiQC

        Per Base N Content

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        Created with MultiQC

        Sequence Length Distribution

        The distribution of fragment sizes (read lengths) found. See the FastQC help

        Created with MultiQC

        Sequence Duplication Levels

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (e.g. PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

        Created with MultiQC

        Overrepresented sequences by sample

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

        Created with MultiQC

        Top overrepresented sequences

        Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

        Showing 20/20 rows and 3/3 columns.
        Overrepresented sequenceReportsOccurrences% of all reads
        CGAATGGGGAGCCCACAGGCCAGCGTCCGGAGCGCGCAGATGCCGAAGCA
        2
        49116
        0.0325%
        GTCGAATGGGGAGCCCACAGGCCAGCGTCCGGAGCGCGCAGATGCCGAAG
        2
        22085
        0.0146%
        CGGGTCGAATGGGGAGCCCACAGGCCAGCGTCCGGAGCGCGCAGATGCCG
        2
        19582
        0.0130%
        CGCGCAGATGCCGAAGCACGCCAGAGGCGCGCGCTGCCTACCACAATCAA
        1
        122418
        0.0810%
        CGCAGATGCCGAAGCACGCCAGAGGCGCGCGCTGCCTACCACAATCAAGG
        1
        50241
        0.0333%
        GCCGGCGATGCGCCCCGGTCGGATGTGGAACGGTGTTGAGCCGGTCCGCC
        1
        41944
        0.0278%
        CGGCGATGCGCCCCGGTCGGATGTGGAACGGTGTTGAGCCGGTCCGCCGA
        1
        41386
        0.0274%
        GGGGAGCCCACAGGCCAGCGTCCGGAGCGCGCAGATGCCGAAGCACGCCA
        1
        21173
        0.0140%
        GGAATTTCTGGTACTTTCAATTTCATGATTGTATTCCAGGCTGAGCACAA
        1
        19277
        0.0128%
        GCACGGTTAATGATATCAGCCCAAGTATTAATTACACGGCCTTGACTGTC
        1
        16555
        0.0110%
        GGTAGATCAAGAAAACTGCGGTAGCAGCTGCAACAGGAGCTGAATATGCA
        1
        15925
        0.0105%
        CACGGTTAATGATATCAGCCCAAGTATTAATTACACGGCCTTGACTGTCA
        1
        15831
        0.0105%
        ATCGACTCGGGGCGTGGACCAGCGTGGATTGGGGGGGCGGCCAAAGCCCG
        1
        15524
        0.0103%
        GGTTAATGATATCAGCCCAAGTATTAATTACACGGCCTTGACTGTCAACT
        1
        14729
        0.0098%
        GCCGAAGCACGCCAGAGGCGCGCGCTGCCTACCACAATCAAGGAGACGAC
        1
        13691
        0.0091%
        CGGATGGGGGCCGGCGATGCGCCCCGGTCGGATGTGGAACGGTGTTGAGC
        1
        13090
        0.0087%
        CAGATGCCGAAGCACGCCAGAGGCGCGCGCTGCCTACCACAATCAAGGAG
        1
        12796
        0.0085%
        GTTGAAACAGATGAAGACAATTTCTTGCTTAGTTATCTAAGAGGGGAAGA
        1
        12474
        0.0083%
        TGGGGGCCGGCGATGCGCCCCGGTCGGATGTGGAACGGTGTTGAGCCGGT
        1
        11940
        0.0079%
        CGTGGATTGGGGGGGCGGCCAAAGCCCGGGCTTTTGATACGCTCGTGGAA
        1
        11858
        0.0079%

        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        Created with MultiQC

        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

        Created with MultiQC

        SAMPLE: fastp

        All-in-one FASTQ preprocessor (QC, adapters, trimming, filtering, splitting...).URL: https://github.com/OpenGene/fastpDOI: 10.1093/bioinformatics/bty560

        Fastp goes through fastq files in a folder and perform a series of quality control and filtering. Quality control and reporting are displayed both before and after filtering, allowing for a clear depiction of the consequences of the filtering process. Notably, the latter can be conducted on a variety of parameters including quality scores, length, as well as the presence of adapters, polyG, or polyX tailing.

        Filtered Reads

        Filtering statistics of sampled reads.

        Created with MultiQC

        Insert Sizes

        Insert size estimation of sampled reads.

        Created with MultiQC

        Sequence Quality

        Average sequencing quality over each base of all reads.

        Created with MultiQC

        GC Content

        Average GC content over each base of all reads.

        Created with MultiQC

        N content

        Average N content over each base of all reads.

        Created with MultiQC

        SAMPLE: FastQC (Post-trimming)

        Quality control tool for high throughput sequencing data.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        Created with MultiQC

        Sequence Quality Histograms

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        Created with MultiQC

        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        Created with MultiQC

        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        Created with MultiQC

        Per Base N Content

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        Created with MultiQC

        Sequence Length Distribution

        The distribution of fragment sizes (read lengths) found. See the FastQC help

        Created with MultiQC

        Sequence Duplication Levels

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (e.g. PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

        Created with MultiQC

        Overrepresented sequences by sample

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

        Created with MultiQC

        Top overrepresented sequences

        Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

        Showing 20/20 rows and 3/3 columns.
        Overrepresented sequenceReportsOccurrences% of all reads
        CGAATGGGGAGCCCACAGGCCAGCGTCCGGAGCGCGCAGATGCCGAAGCA
        2
        40244
        0.0386%
        CGGGTCGAATGGGGAGCCCACAGGCCAGCGTCCGGAGCGCGCAGATGCCG
        2
        16599
        0.0159%
        GTCGAATGGGGAGCCCACAGGCCAGCGTCCGGAGCGCGCAGATGCCGAAG
        2
        18234
        0.0175%
        CGCGCAGATGCCGAAGCACGCCAGAGGCGCGCGCTGCCTACCACAATCAA
        1
        99513
        0.0954%
        CGCAGATGCCGAAGCACGCCAGAGGCGCGCGCTGCCTACCACAATCAAGG
        1
        41012
        0.0393%
        GCCGGCGATGCGCCCCGGTCGGATGTGGAACGGTGTTGAGCCGGTCCGCC
        1
        37616
        0.0360%
        CGGCGATGCGCCCCGGTCGGATGTGGAACGGTGTTGAGCCGGTCCGCCGA
        1
        36808
        0.0353%
        GGGGAGCCCACAGGCCAGCGTCCGGAGCGCGCAGATGCCGAAGCACGCCA
        1
        18104
        0.0173%
        GGAATTTCTGGTACTTTCAATTTCATGATTGTATTCCAGGCTGAGCACAA
        1
        15613
        0.0150%
        ATCGACTCGGGGCGTGGACCAGCGTGGATTGGGGGGGCGGCCAAAGCCCG
        1
        14363
        0.0138%
        GCACGGTTAATGATATCAGCCCAAGTATTAATTACACGGCCTTGACTGTC
        1
        11917
        0.0114%
        GGTAGATCAAGAAAACTGCGGTAGCAGCTGCAACAGGAGCTGAATATGCA
        1
        11302
        0.0108%
        GCCGAAGCACGCCAGAGGCGCGCGCTGCCTACCACAATCAAGGAGACGAC
        1
        11132
        0.0107%
        GGGCCGGCGATGCGCCCCGGTCGGATGTGGAACGGTGTTGAGCCGGTCCG
        1
        11111
        0.0106%
        CACGGTTAATGATATCAGCCCAAGTATTAATTACACGGCCTTGACTGTCA
        1
        10842
        0.0104%
        CAGATGCCGAAGCACGCCAGAGGCGCGCGCTGCCTACCACAATCAAGGAG
        1
        10821
        0.0104%
        GGTTAATGATATCAGCCCAAGTATTAATTACACGGCCTTGACTGTCAACT
        1
        10381
        0.0099%
        CGGATGGGGGCCGGCGATGCGCCCCGGTCGGATGTGGAACGGTGTTGAGC
        1
        10334
        0.0099%
        CGTGGATTGGGGGGGCGGCCAAAGCCCGGGCTTTTGATACGCTCGTGGAA
        1
        10325
        0.0099%
        TGGGGAGCCCACAGGCCAGCGTCCGGAGCGCGCAGATGCCGAAGCACGCC
        1
        9800
        0.0094%

        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        Created with MultiQC

        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

        Created with MultiQC

        SAMPLE: Kraken2 (Host-removal)

        Taxonomic classification using exact k-mer matches to find the lowest common ancestor (LCA) of a given sequence.URL: https://ccb.jhu.edu/software/krakenDOI: 10.1186/gb-2014-15-3-r46

        Top taxa

        The number of reads falling into the top 5 taxa across different ranks.

        To make this plot, the percentage of each sample assigned to a given taxa is summed across all samples. The counts for these top 5 taxa are then plotted for each of the 9 different taxa ranks. The unclassified count is always shown across all taxa ranks.

        The total number of reads is approximated by dividing the number of unclassified reads by the percentage of the library that they account for. Note that this is only an approximation, and that kraken percentages don't always add to exactly 100%.

        The category "Other" shows the difference between the above total read count and the sum of the read counts in the top 5 taxa shown + unclassified. This should cover all taxa not in the top 5, +/- any rounding errors.

        Note that any taxon that does not exactly fit a taxon rank (eg. - or G2) is ignored.

        Created with MultiQC

        SAMPLE: FastQC (post-Host-removal)

        Quality control tool for high throughput sequencing data.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        Created with MultiQC

        Sequence Quality Histograms

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        Created with MultiQC

        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        Created with MultiQC

        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        Created with MultiQC

        Per Base N Content

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        Created with MultiQC

        Sequence Length Distribution

        The distribution of fragment sizes (read lengths) found. See the FastQC help

        Created with MultiQC

        Sequence Duplication Levels

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (e.g. PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

        Created with MultiQC

        Overrepresented sequences by sample

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

        Created with MultiQC

        Top overrepresented sequences

        Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

        Showing 20/20 rows and 3/3 columns.
        Overrepresented sequenceReportsOccurrences% of all reads
        CGAATGGGGAGCCCACAGGCCAGCGTCCGGAGCGCGCAGATGCCGAAGCA
        2
        33774
        0.0395%
        GGGGAGCCCACAGGCCAGCGTCCGGAGCGCGCAGATGCCGAAGCACGCCA
        2
        15291
        0.0179%
        CGGGTCGAATGGGGAGCCCACAGGCCAGCGTCCGGAGCGCGCAGATGCCG
        2
        14205
        0.0166%
        GTCGAATGGGGAGCCCACAGGCCAGCGTCCGGAGCGCGCAGATGCCGAAG
        2
        15658
        0.0183%
        CGCGCAGATGCCGAAGCACGCCAGAGGCGCGCGCTGCCTACCACAATCAA
        1
        33273
        0.0389%
        ATCGACTCGGGGCGTGGACCAGCGTGGATTGGGGGGGCGGCCAAAGCCCG
        1
        14138
        0.0165%
        CGCAGATGCCGAAGCACGCCAGAGGCGCGCGCTGCCTACCACAATCAAGG
        1
        13123
        0.0153%
        CGGCGATGCGCCCCGGTCGGATGTGGAACGGTGTTGAGCCGGTCCGCCGA
        1
        11276
        0.0132%
        CGTGGATTGGGGGGGCGGCCAAAGCCCGGGCTTTTGATACGCTCGTGGAA
        1
        10055
        0.0118%
        GTTGAAACAGATGAAGACAATTTCTTGCTTAGTTATCTAAGAGGGGAAGA
        1
        8966
        0.0105%
        GCCGGTCCGCCGATCGACTCGGGGCGTGGACCAGCGTGGATTGGGGGGGC
        1
        8781
        0.0103%
        CGCCGATCGACTCGGGGCGTGGACCAGCGTGGATTGGGGGGGCGGCCAAA
        1
        8584
        0.0100%
        GGGAAATATAGATACATGATAGAACAATCTCTTTTAGGAGGAGGAGGGAC
        1
        8175
        0.0096%
        TGGGGAGCCCACAGGCCAGCGTCCGGAGCGCGCAGATGCCGAAGCACGCC
        1
        8170
        0.0096%
        CTAAAACATATAATCTCTCCACAGCCAGACTTTACAAATAAATTATATAA
        1
        6793
        0.0079%
        AAAACATATAATCTCTCCACAGCCAGACTTTACAAATAAATTATATAAGA
        1
        6176
        0.0072%
        CAGATATCTATTTTGTTAATGGAATCAAAAAACTACTGTTCAGAATGGAA
        1
        6118
        0.0072%
        CTCGATGGAAATTGTACTTCAAGGCGGCCACCGCGGCTCTTCCGCCGCGA
        1
        6103
        0.0071%
        GTTTAGTTAGAGAAGCCCATCAGGCGTACTGGACTTAGTAAGCACCGGCC
        1
        6039
        0.0071%
        AAGGGAACCAGTTTTGATATAGAAACATTGTTGCGGAACAGTTTTAGACC
        1
        5936
        0.0069%

        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        Created with MultiQC

        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

        Created with MultiQC

        SAMPLE: Quast (Spades)

        Quality assessment tool for genome assemblies.URL: http://quast.bioinf.spbau.ruDOI: 10.1093/bioinformatics/btt086

        Assembly Statistics

        Showing 8/8 rows and 4/4 columns.
        Sample NameN50 (Kbp)L50 (K)Largest contig (Kbp)Length (Mbp)
        DRX474368
        1.1Kbp
        6.7K
        17.9Kbp
        24.7Mbp
        ERX13483332
        0.3Kbp
        0.0K
        0.4Kbp
        0.0Mbp
        SRX16007174
        1.1Kbp
        10.7K
        9.9Kbp
        41.3Mbp
        SRX17981498
        0.3Kbp
        0.1K
        4.3Kbp
        0.1Mbp
        SRX25387944
        0.7Kbp
        30.8K
        9.3Kbp
        70.8Mbp
        SRX26432135
        3.3Kbp
        2.6K
        22.7Kbp
        27.6Mbp
        SRX27477944
        0.7Kbp
        31.9K
        12.1Kbp
        84.1Mbp
        SRX28813238
        0.5Kbp
        18.7K
        17.3Kbp
        35.1Mbp

        Number of Contigs

        This plot shows the number of contigs found for each assembly, broken down by length.

        Created with MultiQC

        SAMPLE: Quast (Megahit)

        Quality assessment tool for genome assemblies.URL: http://quast.bioinf.spbau.ruDOI: 10.1093/bioinformatics/btt086

        Assembly Statistics

        Showing 8/8 rows and 4/4 columns.
        Sample NameN50 (Kbp)L50 (K)Largest contig (Kbp)Length (Mbp)
        DRX474368
        1.3Kbp
        5.3K
        17.6Kbp
        21.6Mbp
        ERX13483332
        0.2Kbp
        0.0K
        0.4Kbp
        0.0Mbp
        SRX16007174
        1.4Kbp
        7.9K
        9.0Kbp
        35.0Mbp
        SRX17981498
        1.1Kbp
        0.0K
        7.3Kbp
        0.0Mbp
        SRX25387944
        1.0Kbp
        19.8K
        8.5Kbp
        63.1Mbp
        SRX26432135
        3.7Kbp
        2.4K
        22.7Kbp
        26.6Mbp
        SRX27477944
        1.1Kbp
        21.3K
        11.0Kbp
        77.4Mbp
        SRX28813238
        0.7Kbp
        9.6K
        16.6Kbp
        24.2Mbp

        Number of Contigs

        This plot shows the number of contigs found for each assembly, broken down by length.

        Created with MultiQC

        CLUSTER: Samtools Stats (Raw)

        Toolkit for interacting with BAM/CRAM files.URL: http://www.htslib.orgDOI: 10.1093/bioinformatics/btp352

        Percent mapped

        Alignment metrics from samtools stats; mapped vs. unmapped reads vs. reads mapped with MQ0.

        For a set of samples that have come from the same multiplexed library, similar numbers of reads for each sample are expected. Large differences in numbers might indicate issues during the library preparation process. Whilst large differences in read numbers may be controlled for in downstream processings (e.g. read count normalisation), you may wish to consider whether the read depths achieved have fallen below recommended levels depending on the applications.

        Low alignment rates could indicate contamination of samples (e.g. adapter sequences), low sequencing quality or other artefacts. These can be further investigated in the sequence level QC (e.g. from FastQC).

        Reads mapped with MQ0 often indicate that the reads are ambiguously mapped to multiple locations in the reference sequence. This can be due to repetitive regions in the genome, the presence of alternative contigs in the reference, or due to reads that are too short to be uniquely mapped. These reads are often filtered out in downstream analyses.

        Created with MultiQC

        Alignment stats

        This module parses the output from samtools stats. All numbers in millions.

        Showing 32 samples.

        Created with MultiQC

        CLUSTER: Picard

        Tools for manipulating high-throughput sequencing data.URL: http://broadinstitute.github.io/picard

        Mark Duplicates

        Number of reads, categorised by duplication state. Pair counts are doubled - see help text for details.

        The table in the Picard metrics file contains some columns referring read pairs and some referring to single reads.

        To make the numbers in this plot sum correctly, values referring to pairs are doubled according to the scheme below:

        • READS_IN_DUPLICATE_PAIRS = 2 * READ_PAIR_DUPLICATES
        • READS_IN_UNIQUE_PAIRS = 2 * (READ_PAIRS_EXAMINED - READ_PAIR_DUPLICATES)
        • READS_IN_UNIQUE_UNPAIRED = UNPAIRED_READS_EXAMINED - UNPAIRED_READ_DUPLICATES
        • READS_IN_DUPLICATE_PAIRS_OPTICAL = 2 * READ_PAIR_OPTICAL_DUPLICATES
        • READS_IN_DUPLICATE_PAIRS_NONOPTICAL = READS_IN_DUPLICATE_PAIRS - READS_IN_DUPLICATE_PAIRS_OPTICAL
        • READS_IN_DUPLICATE_UNPAIRED = UNPAIRED_READ_DUPLICATES
        • READS_UNMAPPED = UNMAPPED_READS
        Created with MultiQC

        CLUSTER: Samtools Stats (Post-dedup)

        Toolkit for interacting with BAM/CRAM files.URL: http://www.htslib.orgDOI: 10.1093/bioinformatics/btp352

        Percent mapped

        Alignment metrics from samtools stats; mapped vs. unmapped reads vs. reads mapped with MQ0.

        For a set of samples that have come from the same multiplexed library, similar numbers of reads for each sample are expected. Large differences in numbers might indicate issues during the library preparation process. Whilst large differences in read numbers may be controlled for in downstream processings (e.g. read count normalisation), you may wish to consider whether the read depths achieved have fallen below recommended levels depending on the applications.

        Low alignment rates could indicate contamination of samples (e.g. adapter sequences), low sequencing quality or other artefacts. These can be further investigated in the sequence level QC (e.g. from FastQC).

        Reads mapped with MQ0 often indicate that the reads are ambiguously mapped to multiple locations in the reference sequence. This can be due to repetitive regions in the genome, the presence of alternative contigs in the reference, or due to reads that are too short to be uniquely mapped. These reads are often filtered out in downstream analyses.

        Created with MultiQC

        Alignment stats

        This module parses the output from samtools stats. All numbers in millions.

        Showing 32 samples.

        Created with MultiQC

        Flagstat

        This module parses the output from samtools flagstat

        Showing 32 samples.

        Created with MultiQC

        CLUSTER: mosdepth

        Fast BAM/CRAM depth calculation for WGS, exome, or targeted sequencing.URL: https://github.com/brentp/mosdepthDOI: 10.1093/bioinformatics/btx699

        Cumulative coverage distribution

        Proportion of bases in the reference genome with, at least, a given depth of coverage. Calculated across the entire genome length

        For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position, while the breadth of coverage is the fraction of the reference sequence to which reads have been mapped with at least a given depth of coverage (Sims et al. 2014).

        Defining coverage breadth in terms of coverage depth is useful, because sequencing experiments typically require a specific minimum depth of coverage over the region of interest (Sims et al. 2014), so the extent of the reference sequence that is amenable to analysis is constrained to lie within regions that have sufficient depth. With inadequate sequencing breadth, it can be difficult to distinguish the absence of a biological feature (such as a gene) from a lack of data (Green 2007).

        For increasing coverage depths (1×, 2×, …, N×), coverage breadth is calculated as the percentage of the reference sequence that is covered by at least that number of reads, then plots coverage breadth (y-axis) against coverage depth (x-axis). This plot shows the relationship between sequencing depth and breadth for each read dataset, which can be used to gauge, for example, the likely effect of a minimum depth filter on the fraction of a genome available for analysis.

        Created with MultiQC

        Average coverage per contig

        Average coverage per contig or chromosome

        Created with MultiQC

        CLUSTER: Bcftools

        Utilities for variant calling and manipulating VCFs and BCFs.URL: https://samtools.github.io/bcftoolsDOI: 10.1093/gigascience/giab008

        Variant Substitution Types

        Created with MultiQC

        Variant Quality

        Created with MultiQC

        Indel Distribution

        Created with MultiQC

        CLUSTER: Total variants (iVar)

        CLUSTER: Total variants (iVar) is calculated from the total number of variants called by iVar.

        Created with MultiQC

        Failed contig quality

        Contigs that are not of minimum size 500 or have more then 50 ambigous bases per 100 kbp were filtered out.

        Showing 4/4 rows and 5/5 columns.
        Idsample nameclusterstepcontig sizeN's %
        DRX474368_cl345
        DRX474368
        cl345
        consensus
        145
        0
        DRX474368_cl393
        DRX474368
        cl393
        consensus
        281
        0
        SRX16007174_cl45
        SRX16007174
        cl45
        consensus
        411
        0
        SRX27477944_cl272
        SRX27477944
        cl272
        consensus
        356
        0

        Cluster Summary

        Number of identified contig clusters per sample after assembly.

        Created with MultiQC

        Software Versions

        Software Versions lists versions of software tools extracted from file contents.

        GroupSoftwareVersion
        BCFTOOLS_CONSENSUSbcftools1.21
        BCFTOOLS_FILTERbcftools1.21
        BCFTOOLS_SORTbcftools1.21
        BCFTOOLS_STATSbcftools1.21
        BEDTOOLS_MASKFASTAbedtools2.31.1
        BEDTOOLS_MERGEbedtools2.31.1
        BLASTN_QCblast2.16.0+
        BLAST_BLASTNblast2.16.0+
        BLAST_FILTERbiopython1.79
        pandas0.25.3
        python3.8.12
        BLAST_MAKEBLASTDBblast2.16.0+
        BWAMEM2_INDEXbwamem22.2.1
        BWAMEM2_MEMbwamem22.2.1
        samtools1.21
        CAT_ASSEMBLERSpigz2.3.4
        CAT_CLUSTERpigz2.3.4
        CHECKV_ENDTOENDcheckv1.0.3
        CONTIG_IDXSTATSsamtools1.21
        CONTIG_INDEXsamtools1.21
        CUSTOM_MPILEUPnumpy1.26.4
        pysam0.22.1
        pysamstats1.1.2
        python3.9.19
        EXTRACT_CLUSTERbiopython1.81
        python3.12.0
        EXTRACT_PRECLUSTERbiopython1.78
        python3.9.1
        FASTPfastp0.24.0
        FASTQC_HOSTfastqc0.12.1
        FASTQC_RAWfastqc0.12.1
        FASTQC_TRIMfastqc0.12.1
        IVAR_CONTIG_CONSENSUSivar1.4.4
        IVAR_VARIANTSivar1.4.4
        samtools1.21
        IVAR_VARIANTS_TO_VCFpython3.9.12
        KAIJU_CONTIGkaiju1.10.0
        KRAKEN2_CONTIGkraken22.1.5
        pigz2.8
        KRAKEN2_HOST_REMOVEkraken22.1.5
        pigz2.8
        MAKE_BED_MASKpython3.9.5
        samtools1.14
        MEGAHITmegahit1.2.9
        MINIMAP2_CONTIG_ALIGNminimap22.29-r1283
        samtools1.21
        MINIMAP2_CONTIG_INDEXminimap22.29-r1283
        MMSEQS_CLUSTERmmseqs17.b804f
        MMSEQS_CREATEANNOTATIONDBmmseqs17.b804f
        MMSEQS_CREATEDBmmseqs17.b804f
        MMSEQS_CREATETSVmmseqs17.b804f
        MMSEQS_EASYSEARCHmmseqs17.b804f
        MOSDEPTHmosdepth0.3.10
        NOCOV_TO_REFERENCEbiopython1.84
        matplotlib3.9.2
        numpy2.1.1
        python3.12.5
        PICARD_COLLECTMULTIPLEMETRICSpicard3.3.0
        PICARD_MARKDUPLICATESpicard3.3.0
        PRINSEQ_CONTIGprinseqplusplus1.2
        QUASTquast5.3.0
        QUAST_QCquast5.3.0
        RENAME_FASTA_HEADER_CALLED_CONSENSUSsed4.7
        RENAME_FASTA_HEADER_CONTIG_CONSENSUSsed4.7
        RENAME_FASTA_HEADER_SINGLETONsed4.7
        SAMTOOLS_FAIDXsamtools1.21
        SAMTOOLS_FLAGSTATsamtools1.21
        SAMTOOLS_IDXSTATSsamtools1.21
        SAMTOOLS_INDEXsamtools1.21
        SAMTOOLS_STATSsamtools1.21
        SPADESspades4.1.0
        TABIX_TABIXtabix1.21
        WorkflowNextflow25.04.3
        nf-core/viralmetagenomev0.1.3dev

        nf-core/viralmetagenome Methods Description

        Suggested text and references to use when describing pipeline usage within the methods section of a publication.URL: https://github.com/nf-core/viralmetagenome

        Methods

        Data was processed using nf-core/viralmetagenome v0.1.3dev of the nf-core collection of workflows (Ewels et al., 2020), utilising reproducible software environments from the Bioconda (Grüning et al., 2018) and Biocontainers (da Veiga Leprevost et al., 2017) projects.

        The pipeline was executed with Nextflow v25.04.3 (Di Tommaso et al., 2017) with the following command:

        nextflow run /vsc-hard-mounts/leuven-data/344/vsc34477/LVE-BIO2-PIPELINE/viralgenie -ansi-log false -with-tower -resume -profile singularity,genius,vsc_kul_uhasselt -params-file plants.params.json --input samplesheet.plant.csv --outdir results-plants

        Tools used in the workflow included: Viralmetagenome (Klaps et al.) nf-core (Ewels et al. 2020) Nextflow (Di Tommaso et al. 2017) Bbduk (Bushnell 2022) BCFtools (Danecek et al. 2021) BLAST+ (Camacho et al. 2009) Bowtie2 (Langmead and Salzberg 2012) BWA-MEM (Li 2013) BWA-MEM2 (Vasimuddin et al. 2019) CD-HIT (Fu et al. 2012) CheckV (Nayfach et al. 2021) FastQC (Andrews 2010) fastp (Chen et al. 2018) HUMID (Laros and van den Berg) iVar (Grubaugh et al. 2019) Kaiju (Menzel et al. 2016) Kraken2 (Wood et al. 2019) Leiden Algorithm (Traag et al. 2019) Mash (Ondov et al. 2016) MEGAHIT (Li et al. 2016) Minimap2 (Li 2018) MMseqs2 (Steinegger and Söding 2017) Mosdepth (Pedersen and Quinlan 2018) MultiQC (Ewels et al. 2016) Picard (Broad Institute) QUAST (Gurevich et al. 2013) SAMtools (Li 2011) SPAdes (Bankevich et al. 2012) SSPACE Basic (Boetzer et al. 2011) Trimmomatic (Bolger et al. 2014) Trinity (Haas et al. 2013) UMI-tools (Smith et al. 2017) vRhyme (Kieft et al. 2022) VSEARCH (Rognes et al. 2016) Anaconda (Anaconda 2016) Bioconda (Grüning et al. 2018) BioContainers (da Veiga Leprevost et al. 2017) Docker (Merkel 2014) Singularity (Kurtzer et al. 2017) .

        References

        • Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. doi: 10.1038/nbt.3820
        • Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. doi: 10.1038/s41587-020-0439-x
        • Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J., & Bioconda Team. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7), 475–476. doi: 10.1038/s41592-018-0046-7
        • da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics (Oxford, England), 33(16), 2580–2582. doi: 10.1093/bioinformatics/btx192
        • Klaps J, Lemey P, Kafetzopoulou L. Viralmetagenome: A metagenomics analysis pipeline for eukaryotic viruses. __Github__ https://github.com/nf-core/viralmetagenome.
        • Ewels PA, Peltzer A, Fillinger S, Patel H, Alneberg J, Wilm A, Garcia MU, Di Tommaso P, Nahnsen S. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. 2020 Mar;38(3):276-278. doi: 10.1038/s41587-020-0439-x. PubMed PMID: 32055031.
        • Di Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E, Notredame C. Nextflow enables reproducible computational workflows. Nat Biotechnol. 2017 Apr 11;35(4):316-319. doi: 10.1038/nbt.3820. PubMed PMID: 28398311.
        • Bushnell B. (2022) BBMap, URL: http://sourceforge.net/projects/bbmap/
        • Danecek, Petr et al. “Twelve years of SAMtools and BCFtools.” GigaScience vol. 10,2 (2021): giab008. doi:10.1093/gigascience/giab008
        • Camacho, Christiam et al. “BLAST+: architecture and applications.” BMC bioinformatics vol. 10 421. 15 Dec. 2009, doi:10.1186/1471-2105-10-421
        • Langmead, Ben, and Steven L Salzberg. “Fast gapped-read alignment with Bowtie 2.” Nature methods vol. 9,4 357-9. 4 Mar. 2012, doi:10.1038/nmeth.1923
        • Li H. (2013) Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv:1303.3997v2.
        • M. Vasimuddin, S. Misra, H. Li and S. Aluru, 'Efficient Architecture-Aware Acceleration of BWA-MEM for Multicore Systems,' 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS), Rio de Janeiro, Brazil, 2019, pp. 314-324, doi: 10.1109/IPDPS.2019.00041.
        • Fu, Limin et al. “CD-HIT: accelerated for clustering the next-generation sequencing data.” Bioinformatics (Oxford, England) vol. 28,23 (2012): 3150-2. doi:10.1093/bioinformatics/bts565
        • Nayfach, Stephen et al. “CheckV assesses the quality and completeness of metagenome-assembled viral genomes.” Nature biotechnology vol. 39,5 (2021): 578-585. doi:10.1038/s41587-020-00774-7
        • Andrews, S. (2010). FastQC: A Quality Control Tool for High Throughput Sequence Data [Online].
        • Chen, Shifu et al. “fastp: an ultra-fast all-in-one FASTQ preprocessor.” Bioinformatics (Oxford, England) vol. 34,17 (2018): i884-i890. doi:10.1093/bioinformatics/bty560
        • Laros J, van den Berg R, __Github__ https://github.com/jfjlaros/HUMID
        • Grubaugh, Nathan D et al. “An amplicon-based sequencing framework for accurately measuring intrahost virus diversity using PrimalSeq and iVar.” Genome biology vol. 20,1 8. 8 Jan. 2019, doi:10.1186/s13059-018-1618-7
        • Menzel, Peter et al. “Fast and sensitive taxonomic classification for metagenomics with Kaiju.” Nature communications vol. 7 11257. 13 Apr. 2016, doi:10.1038/ncomms11257
        • Wood, Derrick E., Jennifer Lu, and Ben Langmead. 2019. Improved Metagenomic Analysis with Kraken 2. Genome Biology 20 (1): 257. doi: 10.1186/s13059-019-1891-0.
        • Traag, V A et al. “From Louvain to Leiden: guaranteeing well-connected communities.” Scientific reports vol. 9,1 5233. 26 Mar. 2019, doi:10.1038/s41598-019-41695-z
        • Ondov, Brian D et al. “Mash: fast genome and metagenome distance estimation using MinHash.” Genome biology vol. 17,1 132. 20 Jun. 2016, doi:10.1186/s13059-016-0997-x
        • Li, Dinghua et al. “MEGAHIT v1.0: A fast and scalable metagenome assembler driven by advanced methodologies and community practices.” Methods (San Diego, Calif.) vol. 102 (2016): 3-11. doi:10.1016/j.ymeth.2016.02.020
        • Li, Heng. “Minimap2: pairwise alignment for nucleotide sequences.” Bioinformatics (Oxford, England) vol. 34,18 (2018): 3094-3100. doi:10.1093/bioinformatics/bty191
        • Steinegger, Martin, and Johannes Söding. “MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets.” Nature biotechnology vol. 35,11 (2017): 1026-1028. doi:10.1038/nbt.3988
        • Pedersen, Brent S, and Aaron R Quinlan. “Mosdepth: quick coverage calculation for genomes and exomes.” Bioinformatics (Oxford, England) vol. 34,5 (2018): 867-868. doi:10.1093/bioinformatics/btx699
        • Ewels, Philip et al. “MultiQC: summarize analysis results for multiple tools and samples in a single report.” Bioinformatics (Oxford, England) vol. 32,19 (2016): 3047-8. doi:10.1093/bioinformatics/btw354
        • Gurevich, Alexey et al. “QUAST: quality assessment tool for genome assemblies.” Bioinformatics (Oxford, England) vol. 29,8 (2013): 1072-5. doi:10.1093/bioinformatics/btt086
        • Li H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics. 2011 Nov 1;27(21):2987-93. doi: 10.1093/bioinformatics/btr509. Epub 2011 Sep 8. PMID: 21903627; PMCID: PMC3198575.
        • Bankevich, Anton et al. “SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing.” Journal of computational biology : a journal of computational molecular cell biology vol. 19,5 (2012): 455-77. doi:10.1089/cmb.2012.0021
        • Boetzer, Marten et al. “Scaffolding pre-assembled contigs using SSPACE.” Bioinformatics (Oxford, England) vol. 27,4 (2011): 578-9. doi:10.1093/bioinformatics/btq683
        • Bolger, Anthony M et al. “Trimmomatic: a flexible trimmer for Illumina sequence data.” Bioinformatics (Oxford, England) vol. 30,15 (2014): 2114-20. doi:10.1093/bioinformatics/btu170
        • Haas, Brian J et al. “De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis.” Nature protocols vol. 8,8 (2013): 1494-512. doi:10.1038/nprot.2013.084
        • Smith, Tom et al. “UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy.” Genome research vol. 27,3 (2017): 491-499. doi:10.1101/gr.209601.116
        • Kieft, Kristopher et al. “vRhyme enables binning of viral genomes from metagenomes.” Nucleic acids research vol. 50,14 (2022): e83. doi:10.1093/nar/gkac341
        • Rognes, Torbjørn et al. “VSEARCH: a versatile open source tool for metagenomics.” PeerJ vol. 4 e2584. 18 Oct. 2016, doi:10.7717/peerj.2584
        • Anaconda Software Distribution. Computer software. Vers. 2-2.4.0. Anaconda, Nov. 2016. Web.
        • Grüning B, Dale R, Sjödin A, Chapman BA, Rowe J, Tomkins-Tinch CH, Valieris R, Köster J; Bioconda Team. Bioconda: sustainable and comprehensive software distribution for the life sciences. Nat Methods. 2018 Jul;15(7):475-476. doi: 10.1038/s41592-018-0046-7. PubMed PMID: 29967506.
        • da Veiga Leprevost F, Grüning B, Aflitos SA, Röst HL, Uszkoreit J, Barsnes H, Vaudel M, Moreno P, Gatto L, Weber J, Bai M, Jimenez RC, Sachsenberg T, Pfeuffer J, Alvarez RV, Griss J, Nesvizhskii AI, Perez-Riverol Y. BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics. 2017 Aug 15;33(16):2580-2582. doi: 10.1093/bioinformatics/btx192. PubMed PMID: 28379341; PubMed Central PMCID: PMC5870671.
        • Merkel, D. (2014). Docker: lightweight linux containers for consistent development and deployment. Linux Journal, 2014(239), 2. doi: 10.5555/2600239.2600241.
        • Kurtzer GM, Sochat V, Bauer MW. Singularity: Scientific containers for mobility of compute. PLoS One. 2017 May 11;12(5):e0177459. doi: 10.1371/journal.pone.0177459. eCollection 2017. PubMed PMID: 28494014; PubMed Central PMCID: PMC5426675.
        Notes:
        • If available, make sure to update the text to include the Zenodo DOI of version of the pipeline used.
        • The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!
        • You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.

        nf-core/viralmetagenome Workflow Summary

        - this information is collected when the pipeline is started.URL: https://github.com/nf-core/viralmetagenome

        Input/output options

        input
        samplesheet.plant.csv
        outdir
        results-plants

        Preprocessing options

        arguments_umitools_extract
        --umi-separator ":"
        host_k2_db
        /lustre1/project/stg_00132/databases/kraken2/k2_host_contamination

        Metagenomic diversity

        kaiju_db
        /lustre1/project/stg_00132/databases/kaiju/kaiju_db_rvdb_2024-12-20
        kraken2_db
        /lustre1/project/stg_00132/databases/kraken2/kraken2_viral_C-RVDBv26_15-09-2023
        skip_read_classification
        true

        Polishing

        arguments_extract_precluster
        --simplification-level genus --keep-unclassified false
        cluster_method
        mmseqs-cluster
        keep_unclassified
        false
        reference_pool
        /lustre1/project/stg_00132/databases/RVDB/U-RVDBv29.0.fasta

        Variant analysis

        arguments_bcftools_mpileup3
        --include 'INFO/DP>=5'
        arguments_snpsift_extractfields
        -s "," -e "."
        arguments_umitools_dedup
        --umi-separator=':' --method cluster --unmapped-reads use

        Consensus QC

        annotation_db
        /lustre1/project/stg_00132/databases/virosaurus/virosaurus98_vertebrate-20200330.lasv-lineages.fas
        checkv_db
        /lustre1/project/stg_00132/databases/checkv/checkv-db-v1.5

        Institutional config options

        config_profile_contact
        GitHub: @Joon-Klaps - Email: joon.klaps@kuleuven.be
        config_profile_description
        genius profile for use on the genius cluster of the VSC HPC.
        config_profile_url
        https://docs.vscentrum.be/en/latest/index.html

        Generic options

        trace_report_suffix
        2025-06-22_12-59-08

        Core Nextflow options

        configFiles
        /vsc-hard-mounts/leuven-data/344/vsc34477/LVE-BIO2-PIPELINE/viralgenie/nextflow.config
        containerEngine
        singularity
        launchDir
        /lustre1/project/stg_00132/jklaps/viralgenie-manuscript/analysis/viralgenie
        profile
        singularity,genius,vsc_kul_uhasselt
        projectDir
        /vsc-hard-mounts/leuven-data/344/vsc34477/LVE-BIO2-PIPELINE/viralgenie
        runName
        mighty_majorana
        userName
        vsc34477
        workDir
        /scratch/leuven/344/vsc34477/work